YOLO Object Detection
with Azure Databricks

An Image Processing pipeline designed and developed in Azure Databricks environment. Image Acquisition and Pre-processing techniques is followed by an Object Detector model (YOLO V7). The pipeline implemented had a 92% detection accuracy in detected sanitary pads.

Advanced Data Monitoring
of Etcher tool health

Analysis of the usage of semiconductor etcher tools using feature extraction Self-Organizing Map (SOM). Minimum Quantization Error (MQE) and Squared Predicted Error (SPE) algorithms were used for cross validation. Principal Component Analysis(PCA) was used for Feature Reduction. Finally, a suport vector machine (SVM) algorithm to assess the extent of degradation of the tool.